Load observed net-level metrics:
Load null model derived net-level metrics:
Select only metrics of interest:
Convert dataframes to long format:
Summarize null model metrics:
Check null-models metrics distribution:
Plot to compare network types:
Summary metrics:
## # A tibble: 12 × 5
## # Groups: metric [6]
## metric type obs nulls diff
## <fct> <chr> <chr> <chr> <chr>
## 1 Connectance ind 0.3 ± 0.1 0.48 ± 0.01 -0.18 ± 0.12
## 2 Connectance sp 0.29 ± 0.13 0.42 ± 0.01 -0.13 ± 0.1
## 3 Weighted NODF ind 30.37 ± 12.13 34.29 ± 4.13 -3.91 ± 7.37
## 4 Weighted NODF sp 29.66 ± 11.53 35.56 ± 3.52 -5.89 ± 6.09
## 5 Modularity ind 0.32 ± 0.12 0.26 ± 0.03 0.06 ± 0.06
## 6 Modularity sp 0.37 ± 0.1 0.25 ± 0.03 0.12 ± 0.08
## 7 Interaction evenness ind 0.67 ± 0.07 0.68 ± 0.01 -0.02 ± 0.02
## 8 Interaction evenness sp 0.62 ± 0.07 0.66 ± 0.01 -0.03 ± 0.02
## 9 Assortativity ind -0.48 ± 0.17 -0.51 ± 0.05 0.03 ± 0.08
## 10 Assortativity sp -0.5 ± 0.15 -0.49 ± 0.05 -0.01 ± 0.07
## 11 Centralization ind 0.88 ± 0.07 0.86 ± 0.02 0.02 ± 0.04
## 12 Centralization sp 0.91 ± 0.05 0.89 ± 0.01 0.03 ± 0.04
Plot comparing metrics by network one by one:
Alternative fig 2:
2nd Alternative fig 2:
3rd alternative to fig:
Model comparisions:
## Family: gaussian ( identity )
## Formula: diff ~ type + (1 | net_code)
## Data: filter(tmp2, metric == "connectance")
##
## AIC BIC logLik deviance df.resid
## -592953.2 -592914.9 296480.6 -592961.2 104996
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## net_code (Intercept) 0.0118810 0.10900
## Residual 0.0002043 0.01429
## Number of obs: 105000, groups: net_code, 105
##
## Dispersion estimate for gaussian family (sigma^2): 0.000204
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.17540 0.01607 -10.914 <2e-16 ***
## typesp 0.04574 0.02144 2.134 0.0329 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.02 0.02 0.02 0.024 0.024 0.02 0.024 0.02 0.024 0.02 0.024 0.02 0.016 0.02 0.02 0.02 0.02 0.024 0.024 0.028 ...
## Family: gaussian ( identity )
## Formula: diff ~ type + (1 | net_code)
## Data: filter(tmp2, metric == "weighted.NODF")
##
## AIC BIC logLik deviance df.resid
## 590602.9 590641.2 -295297.5 590594.9 104996
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## net_code (Intercept) 43.74 6.614
## Residual 16.10 4.013
## Number of obs: 105000, groups: net_code, 105
##
## Dispersion estimate for gaussian family (sigma^2): 16.1
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -3.9137 0.9753 -4.013 6e-05 ***
## typesp -1.9772 1.3011 -1.520 0.129
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.964 0.984 0.912 0.972 0.98 0.98 0.992 0.98 0.964 0.98 0.944 0.984 0.98 0.976 0.96 0.956 0.988 0.976 0.996 0.972 ...
## Family: gaussian ( identity )
## Formula: diff ~ type + (1 | net_code)
## Data: filter(tmp2, metric == "M")
##
## AIC BIC logLik deviance df.resid
## -453899.2 -453861.0 226953.6 -453907.2 104996
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## net_code (Intercept) 0.0049470 0.07034
## Residual 0.0007697 0.02774
## Number of obs: 105000, groups: net_code, 105
##
## Dispersion estimate for gaussian family (sigma^2): 0.00077
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.06417 0.01037 6.187 6.12e-10 ***
## typesp 0.05588 0.01384 4.039 5.37e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.428 0.448 0.524 0.384 0.392 0.552 0.508 0.34 0.472 0.416 0.404 0.504 0.284 0.476 0.496 0.328 0.5 0.54 0.148 0.404 ...
## Family: gaussian ( identity )
## Formula: diff ~ type + (1 | net_code)
## Data: filter(tmp2, metric == "interaction.evenness")
##
## AIC BIC logLik deviance df.resid
## -696284.8 -696246.6 348146.4 -696292.8 104996
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## net_code (Intercept) 4.714e-04 0.021713
## Residual 7.652e-05 0.008748
## Number of obs: 105000, groups: net_code, 105
##
## Dispersion estimate for gaussian family (sigma^2): 7.65e-05
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.017300 0.003202 -5.403 6.54e-08 ***
## typesp -0.015001 0.004271 -3.512 0.000444 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.708 0.776 0.676 0.784 0.776 0.716 0.752 0.808 0.724 0.748 0.78 0.732 0.832 0.756 0.692 0.792 0.776 0.688 0.868 0.772 ...
## Family: gaussian ( identity )
## Formula: diff ~ type + (1 | net_code)
## Data: filter(tmp2, metric == "assortativity")
##
## AIC BIC logLik deviance df.resid
## -315165.3 -315127.0 157586.6 -315173.3 104996
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## net_code (Intercept) 0.005890 0.07674
## Residual 0.002888 0.05374
## Number of obs: 105000, groups: net_code, 105
##
## Dispersion estimate for gaussian family (sigma^2): 0.00289
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.03449 0.01132 3.047 0.00231 **
## typesp -0.04626 0.01510 -3.064 0.00219 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.632 0.708 0.72 0.82 0.824 0.728 0.76 0.472 0.608 0.66 0.568 0.628 0.704 0.76 0.776 0.736 0.44 0.608 0.696 0.656 ...
## Family: gaussian ( identity )
## Formula: diff ~ type + (1 | net_code)
## Data: filter(tmp2, metric == "centralization.w")
##
## AIC BIC logLik deviance df.resid
## -511968.2 -511929.9 255988.1 -511976.2 104996
##
## Random effects:
##
## Conditional model:
## Groups Name Variance Std.Dev.
## net_code (Intercept) 0.001508 0.03883
## Residual 0.000443 0.02105
## Number of obs: 105000, groups: net_code, 105
##
## Dispersion estimate for gaussian family (sigma^2): 0.000443
##
## Conditional model:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.022303 0.005726 3.895 9.83e-05 ***
## typesp 0.003934 0.007639 0.515 0.607
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Object of Class DHARMa with simulated residuals based on 250 simulations with refit = FALSE . See ?DHARMa::simulateResiduals for help.
##
## Scaled residual values: 0.232 0.26 0.268 0.252 0.26 0.272 0.28 0.192 0.252 0.252 0.3 0.276 0.22 0.22 0.268 0.208 0.228 0.268 0.248 0.252 ...